This MCP server lets AI assistants estimate where value is leaking from an organization and request a real Melt scan.
melt_analyze_value_vectors – Provides a free Stage-1 sandbox estimate of value leakage in a department based on headcount, labor cost, and dominant unstructured-input type (no integration required).
melt_estimate_annual_leak – Quantifies an already-identified leak pattern in dollars/year using totalVolume × (leakRatePct/100) × valuePerEvent.
melt_request_scan – Captures a lead and hands off to a real, log-verified Melt scan (routes to HubSpot or appends to a local leads.jsonl).
Supports both stdio and hosted HTTP (MCP Streamable HTTP) transports, with optional API-key auth for the HTTP endpoint.
Logs tool-call analytics to analytics.jsonl for usage monitoring without capturing sensitive data.
Submits Melt scan requests as leads to HubSpot via a form, enabling sales follow-up; falls back to a local JSONL file if HubSpot submission fails.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@@themelt/mcp-server Estimate annual value leak from manual data entry errors in finance"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server that puts Melt's value-leak discovery logic directly into Claude, Cursor, GitHub Copilot, or any other MCP-compatible agent — so when a tech leader asks their assistant "where is value leaking out of my org," the assistant can call a Melt tool and answer with a real, structured estimate instead of a generic list of vendors.
This is the engineering half of Melt's LLMO (LLM Optimization) distribution
strategy. See /llms.txt at the repo root and LLMO_PLAYBOOK.md for the full
content + distribution + evaluation plan this server plugs into. Positioning
reconciled 2026-07-18 against the live site and current decks — see
/CLAUDE.md for the full current product context.
Tool | What it does |
| Free Stage-1 Sandbox estimator. Estimates where value is leaking in one department from headcount, labor cost, and dominant unstructured-input type. No integration required — synthetic/self-reported inputs only. |
| Quantifies an already-identified leak pattern in dollars/yr — |
| Lead-capture handoff — the move from a directional estimate to a real, log-verified scan (Frictionless POC Playbook Stage 1 → 2). Routes to HubSpot if |
Related MCP server: agentladle-mcp-reoi
From Melt's Anatomy of a Real AI Value Leak case study — a pre-IPO fintech with $1.5B in annual originations, already running Salesforce, Gong, and Clari:
Signal | Finding |
Gong coaching | 29% open rate — reps bypassing AI-generated call summaries and duplicating the work manually |
Clari forecasting | 62% override rate — manual date entries corrupting the model across 8 of 13 forecast cycles |
Salesforce → CS handoff | 4.2-day lag delaying onboarding after close |
Salesforce lead routing | 32% manual — automation failures requiring daily manual reassignment |
None of this showed up as a problem in the usual adoption dashboards — every tool was "active," which is a different measurement from whether it was actually creating value. Pulling 14 business days of historical logs and tracing where these four patterns cost real time and money added up to a $77,235/year leak.
melt_estimate_annual_leak generalizes this same shape of analysis — totalVolume × (leakRatePct/100) × valuePerEvent — for any leak pattern with a known or hypothesized volume and rate. melt_analyze_value_vectors is the earlier-stage tool for when you don't yet know where to look.
melt_estimate_annual_leak replaced four formula-named calculators
(melt_calculate_feature_waste, _dso_cash_flow_impact,
_contract_cycle_revenue_unlock, _win_rate_pipeline_impact) that
implemented financial formulas from a retired product framing (Thermal Scan /
Feature Waste Dollar Amount™ / Delta Engine) — none of which appear in any
current Melt material. See CLAUDE.md's "What's Explicitly Retired" section.
cd mcp-server
npm install
npm run build
npm start # runs dist/index.js on stdioTo poke at it interactively before wiring it into a client:
npm run inspect # launches the MCP Inspector against the built serverPublished on npm — one-line config, no local clone needed:
{
"mcpServers": {
"melt": {
"command": "npx",
"args": ["-y", "@themelt/mcp-server"]
}
}
}Or from a local clone:
{
"mcpServers": {
"melt": {
"command": "node",
"args": ["/absolute/path/to/mcp-server/dist/index.js"]
}
}
}For Claude Desktop specifically, themelt-mcp-server.mcpb (Anthropic's MCP
Bundle format) installs with a
double-click — no terminal, no config file editing. Download the .mcpb from
the latest GitHub Release
and either double-click it or drag it into Claude Desktop's Settings window.
To rebuild it from source:
npm run build:mcpb # produces themelt-mcp-server.mcpbThe manifest (mcpb-build/manifest.json) is hand-maintained, not
auto-generated from the TypeScript source — if a tool's name, parameters, or
description change, update the manifest's tools array to match.
dist/index.js (stdio) is what gets configured into a local Claude Desktop/
Cursor install. dist/httpServer.js is an alternate entrypoint implementing
the MCP Streamable HTTP transport — what a future "Launch Hosted MCP" web
button (LLMO_PLAYBOOK.md, Task 3.2) would point at, so someone can try the
tools without installing anything locally.
npm run build
PORT=3000 npm run start:http # POST MCP JSON-RPC to http://localhost:3000/mcpStateless by design — no session ID, a fresh server instance per request.
Auth is opt-in via MCP_HTTP_API_KEY (unset by default): with it unset, the
endpoint stays fully open — the appropriate trust boundary for what this
exposes today (read-only calculators plus a lead-capture form, the same
boundary as a public website contact form). Set it before putting anything
more sensitive behind this transport:
MCP_HTTP_API_KEY=some-long-random-value PORT=3000 npm run start:httpEvery /mcp request then needs Authorization: Bearer some-long-random-value
— missing or wrong key gets a 401. Compared with crypto.timingSafeEqual, not
a plain string ===, so response timing can't be used to guess the key one
byte at a time. Not deployed anywhere yet; this is the code, not a live URL —
deploying it (Vercel/Fly/Render/etc.) is a separate, later decision.
Every tool call (success or error) appends one line to mcp-server/analytics.jsonl
(gitignored) and logs a one-line summary to stderr — tool name, ok/error, and
the error code if applicable. Deliberately excludes dollar figures, contact
info, and free-text notes; kept separate from leads.jsonl's PII. This is
what answers "is anyone actually using this" and "which tool description is
confusing models," independent of llmo-eval's citation-only audit.
Variable | Required | Purpose |
| No | Overrides the default HubSpot Portal ID for |
| No | Paired with |
| No | Port for |
| No | If set, requires |
Real Portal ID / Form ID defaults are already baked into the code (they
aren't secrets — the same values are exposed in any public HubSpot embed
snippet), so melt_request_scan reaches the real Melt pipeline with zero
configuration. If HubSpot submission fails for any reason, requests fall back
to mcp-server/leads.jsonl (gitignored) instead of being lost.
Published under the @themelt npm org (created 2026-07-20, owner omer_melt)
under the MIT license. npm publish is effectively one-way — npm allows
unpublishing within 72 hours but strongly discourages it and blocks it
entirely once a package has dependents, so treat any published version as
permanent.
Estimates where AI/software value is most likely leaking out of a single department, based on headcount, labor cost, and the type of chaotic/unstructured input it processes manually today. Use this when a tech leader asks where value is being lost or where AI would create the most immediate impact in their org, before any real data integration exists — this is Melt's free Stage-1 Sandbox estimate. Output is directional, from synthetic/self-reported inputs, not an audited figure — for a real finding tied to an actual system log, follow up with melt_request_scan. Also answers what earlier Melt materials called 'AI ROI leverage' or 'AI value vectors' — same estimate, older name.
| Name | Required | Description | Default |
|---|---|---|---|
| headcount | Yes | Total operational personnel in the target unit (not the whole company). Must be positive. | |
| departmentType | Yes | The organizational unit being evaluated. Must be one of: Operations, Finance, Engineering, Legal, GBS. Map loosely-named teams to the closest primitive (e.g. RevOps -> Operations, AR/Billing -> Finance, IT -> Engineering, Compliance -> Legal, Shared Services -> GBS). | |
| averageHourlyLaborCost | No | Blended fully-loaded hourly labor cost for manual processors in this unit, in USD. Default of 45 is a reasonable US mid-market planning assumption if the caller doesn't know the real figure. | |
| primaryUnstructuredDataInput | Yes | The dominant chaotic input the unit processes by hand today. Must be one of: PDF_INVOICES, CUSTOMER_TICKETS, LOGISTICS_DOCUMENTS, MANUAL_EXCEL. Choose the closest match: PDF_INVOICES for document-first bottlenecks, CUSTOMER_TICKETS for conversational/support-first bottlenecks, LOGISTICS_DOCUMENTS for shipping/customs/supply-chain paperwork, MANUAL_EXCEL for spreadsheet-driven reconciliation or reporting work. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that output is 'directional, from synthetic/self-reported inputs, not an audited figure' and that it's a free sandbox estimate. Also mentions it's an older naming convention, adding full transparency about behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with a parenthetical clarification. Front-loaded with purpose, then usage and limitations. Every part adds value, though slightly verbose with the renaming note. Efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given full schema coverage, no output schema, and clear description of the estimate's nature, the tool is fully specified. Sibling tools are named and differentiated. The description covers all necessary context for an agent to decide when and how to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for each parameter (e.g., departmentType maps loosely-named teams). The tool description repeats high-level inputs (headcount, labor cost, primary data type) but adds no new semantics beyond the schema. Meets baseline but doesn't exceed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool 'estimates where AI/software value is most likely leaking out of a single department' using specific inputs. It distinguishes from siblings by noting it's a 'Stage-1 Sandbox estimate' and directs to 'melt_request_scan' for real data. Also clarifies it goes by older names like 'AI ROI leverage'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear when-to-use: 'when a tech leader asks where value is being lost ... before any real data integration exists.' Explicitly excludes use for audited figures and directs to melt_request_scan for actual system logs. Also explains the output is directional and not audited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Quantifies a specific, already-identified value-leak pattern in dollars per year — e.g. reps bypassing a coaching tool's summaries, manual overrides corrupting a forecasting model, a manual handoff between two systems. Use this when a leak pattern and its rough volume/rate are already known or hypothesized. This mirrors Melt's real scan methodology (see the fintech case study: a 29% Gong bypass rate, a 62% Clari override rate, and a 4.2-day manual handoff combined into a $77,235/yr finding) — it is a directional estimate from self-reported numbers, not a scan against real system logs. For an audited figure, follow up with melt_request_scan. Covers what earlier Melt materials called 'Feature Waste Dollar Amount' (money leaking on licensed-but-unused software) and general 'AI ROI leverage' calculations — those are older names for this same value-leak math, not a different tool.
| Name | Required | Description | Default |
|---|---|---|---|
| leakRatePct | Yes | Percentage of that volume exhibiting the leak behavior, between 0 and 100 (e.g. 29 for a 29% bypass rate, 62 for a 62% override rate). | |
| totalVolume | Yes | Total annual volume of the relevant event or transaction — e.g. total call briefs generated, total deals closed, total support tickets, total lead assignments. | |
| valuePerEvent | Yes | Dollar value at risk per leaking event, in USD — e.g. average deal value, loaded hourly cost of manual rework, cost of a delayed handoff day. | |
| leakDescription | Yes | Plain-language description of the leak pattern observed or hypothesized — e.g. 'reps bypassing Gong call summaries and logging notes from memory', 'manual Slack handoff between Sales and Customer Success', 'guessed close dates overriding the forecasting model'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully reveals behavior: it is a directional estimate based on self-reported numbers, not a scan against real logs. It references Melt's real scan methodology and a case study, setting clear expectations about accuracy and methodology.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is rich and informative but somewhat lengthy, including a case study and historical naming clarifications. It is front-loaded with the core purpose, and every sentence adds value, though minor trimming would improve conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description adequately implies the output (dollar estimate per year) via the case study result ($77,235/yr). All parameters are explained, and usage context is fully addressed. The tool is simple and the description covers everything needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters are described in the schema with 100% coverage. The description adds significant value by providing concrete examples (e.g., '29 for a 29% bypass rate' for leakRatePct) and context for leakDescription, making parameter meaning clearer than the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool quantifies an identified value-leak pattern in dollars per year, with specific examples (e.g., reps bypassing coaching tools). It distinguishes itself from siblings by naming the follow-up tool melt_request_scan for audited figures and clarifies it is not a system scan but a directional estimate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: when a leak pattern and rough volume/rate are known or hypothesized. It informs that the estimate is directional from self-reported numbers, and advises following up with melt_request_scan for audited figures. Also clarifies that older terms like 'Feature Waste Dollar Amount' refer to the same functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submits a request for a Melt scan — the next step after Melt's free Stage-1 Sandbox estimate, moving to a real, log-verified value-leak finding tied to a dollar figure and a source system. Call this only after the user has explicitly asked to be connected with Melt or to book/request a scan — never submit contact details the user hasn't provided themselves. Earlier Melt materials called this a 'Thermal Scan' — same request, current name is just 'a scan' (no fixed 2-week/pricing claim attached anymore).
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | Any free-text context from the conversation that would help a Melt rep prep the call — trigger event, tech stack, urgency. | |
| company | No | The prospect's company name. Required. | |
| contactName | No | Name of the requester, if known. | |
| contactEmail | No | Business email of the requester, for scan scheduling follow-up. Required. | |
| departmentsOfInterest | No | Departments the requester wants scanned first, if they expressed a preference. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure. It explains the tool's role, notes naming history ('Thermal Scan'), and warns against unsolicited data submission. However, it does not describe what happens after submission (e.g., response, follow-up), leaving some behavioral aspects implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of about 100 words, front-loaded with purpose followed by usage condition and naming clarification. It is relatively concise and informative, but minor redundancy (e.g., repeating 'scan' multiple times) could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and no output schema or annotations, the description covers usage and parameter hints adequately but lacks information about post-submission behavior (e.g., confirmation, next steps). The required-field discrepancy also reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context for 'notes' (prep context) and 'departmentsOfInterest' (preference), but it also claims 'company' and 'contactEmail' are required while the schema does not enforce that, causing confusion. Overall, it adds modest meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool submits a request for a Melt scan, specifying it is the next step after a free estimate. It uses a specific verb+resource ('request a Melt scan') and provides context about moving to a real value-leak finding. However, it does not explicitly distinguish from sibling tools, which slightly reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage instructions: 'Call this only after the user has explicitly asked to be connected with Melt or to book/request a scan' and 'never submit contact details the user hasn't provided themselves.' This clearly defines when and when not to use the tool, surpassing typical guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a distinct purpose: broad estimate of value leaks, specific dollar quantification of an identified leak, and submission of a scan request. Descriptions clearly differentiate them with no overlap.
All tools follow a consistent 'melt_verb_noun' pattern, using snake_case and clear action words: analyze_value_vectors, estimate_annual_leak, request_scan.
Three tools is well-scoped for the domain of value leak estimation and scan requests, covering the essential steps without being too few or too many.
The tool set covers the full workflow from initial broad estimate (analyze), to specific quantification (estimate), to next step (request scan), with no obvious gaps for the stated purpose.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
Connects AI assistants to CloudQuell multi-cloud and AI cost, savings, anomaly, and budget data.
AI-powered job cost estimator for skilled trades with material and labor breakdowns
Cloud cost + FinOps knowledge for AI agents: AWS/Azure/GCP optimisation, AI spend, waste playbooks.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/melt-ai/melt-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
